class_docstr: This class specifies the generation algorithm for a Sobol generator.
  
  As Sobol does not make use of a model, it does not implement
  the fit or predict methods.
  
  Attributes:
      init_position: The initial state of the Sobol generator.
          Starts at 0 by default.
      scramble: If True, permutes the parameter values among
          the elements of the Sobol sequence. Default is True.
      See base `RandomModel` for a description of remaining attributes.
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code: def __init__(
      self,
      seed: Optional[int] = None,
      deduplicate: bool = True,
      init_position: int = 0,
      scramble: bool = True,
      generated_points: Optional[np.ndarray] = None,
      fallback_to_sample_polytope: bool = False,
  ) -> None:
      super().__init__(
          deduplicate=deduplicate,
          seed=seed,
          generated_points=generated_points,
          fallback_to_sample_polytope=fallback_to_sample_polytope,
      )
      self.init_position = init_position
      self.scramble = scramble
      # Initialize engine on gen.
      self._engine: Optional[SobolEngine] = None
  
file_path: Ax/ax/models/random/sobol.py
imports: from typing import Any, Callable, Dict, List, Optional, Tuple
  import numpy
  import torch
  from ax.models.base import Model
  from ax.models.model_utils import tunable_feature_indices
  from ax.models.random.base import RandomModel
  from ax.models.types import TConfig
  from ax.utils.common.docutils import copy_doc
  from ax.utils.common.typeutils import not_none
  from torch.quasirandom import SobolEngine
label_desc: Use image node_id 1 to create a new SobolGenerator object from inherited base classes: RandomModel with example: obj = SobolGenerator(seed, deduplicate, init_position, scramble, generated_points, fallback_to_sample_polytope)